{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LWJ6A4ZBWS4VS5GMXRDTN4D6CP","short_pith_number":"pith:LWJ6A4ZB","schema_version":"1.0","canonical_sha256":"5d93e07321b4b95974ccbc4736f07e13e7f954f33c2a6e04932ced9aa9874422","source":{"kind":"arxiv","id":"2311.16030","version":1},"attestation_state":"computed","paper":{"title":"Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"cs.AI","authors_text":"Jueming Hu, Peng Zhao, Yongming Liu, Yutian Pang","submitted_at":"2023-11-27T17:50:14Z","abstract_excerpt":"This paper addresses aircraft delays, emphasizing their impact on safety and financial losses. To mitigate these issues, an innovative machine learning (ML)-enhanced landing scheduling methodology is proposed, aiming to improve automation and safety. Analyzing flight arrival delay scenarios reveals strong multimodal distributions and clusters in arrival flight time durations. A multi-stage conditional ML predictor enhances separation time prediction based on flight events. ML predictions are then integrated as safety constraints in a time-constrained traveling salesman problem formulation, sol"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2311.16030","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2023-11-27T17:50:14Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"9750ddf31ac2d70a5965cedbdae7c8010f96455d81b2289da807fb1ef2f4cc6d","abstract_canon_sha256":"c43904bed3ebe72f5e6ee310633586dd744870595130e0cc841fb2784cd8c774"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:17:12.761190Z","signature_b64":"UikHcAeDQxHAt5xyhegij0Ajyu1dC9UsfHCAqhg1BmCDNrF98M3SsxbANESN71hYroqyvTODzYNWEgazKx8RDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d93e07321b4b95974ccbc4736f07e13e7f954f33c2a6e04932ced9aa9874422","last_reissued_at":"2026-07-05T07:17:12.760805Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:17:12.760805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"cs.AI","authors_text":"Jueming Hu, Peng Zhao, Yongming Liu, Yutian Pang","submitted_at":"2023-11-27T17:50:14Z","abstract_excerpt":"This paper addresses aircraft delays, emphasizing their impact on safety and financial losses. To mitigate these issues, an innovative machine learning (ML)-enhanced landing scheduling methodology is proposed, aiming to improve automation and safety. Analyzing flight arrival delay scenarios reveals strong multimodal distributions and clusters in arrival flight time durations. A multi-stage conditional ML predictor enhances separation time prediction based on flight events. ML predictions are then integrated as safety constraints in a time-constrained traveling salesman problem formulation, sol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16030","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.16030/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2311.16030","created_at":"2026-07-05T07:17:12.760852+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.16030v1","created_at":"2026-07-05T07:17:12.760852+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16030","created_at":"2026-07-05T07:17:12.760852+00:00"},{"alias_kind":"pith_short_12","alias_value":"LWJ6A4ZBWS4V","created_at":"2026-07-05T07:17:12.760852+00:00"},{"alias_kind":"pith_short_16","alias_value":"LWJ6A4ZBWS4VS5GM","created_at":"2026-07-05T07:17:12.760852+00:00"},{"alias_kind":"pith_short_8","alias_value":"LWJ6A4ZB","created_at":"2026-07-05T07:17:12.760852+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LWJ6A4ZBWS4VS5GMXRDTN4D6CP","json":"https://pith.science/pith/LWJ6A4ZBWS4VS5GMXRDTN4D6CP.json","graph_json":"https://pith.science/api/pith-number/LWJ6A4ZBWS4VS5GMXRDTN4D6CP/graph.json","events_json":"https://pith.science/api/pith-number/LWJ6A4ZBWS4VS5GMXRDTN4D6CP/events.json","paper":"https://pith.science/paper/LWJ6A4ZB"},"agent_actions":{"view_html":"https://pith.science/pith/LWJ6A4ZBWS4VS5GMXRDTN4D6CP","download_json":"https://pith.science/pith/LWJ6A4ZBWS4VS5GMXRDTN4D6CP.json","view_paper":"https://pith.science/paper/LWJ6A4ZB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.16030&json=true","fetch_graph":"https://pith.science/api/pith-number/LWJ6A4ZBWS4VS5GMXRDTN4D6CP/graph.json","fetch_events":"https://pith.science/api/pith-number/LWJ6A4ZBWS4VS5GMXRDTN4D6CP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LWJ6A4ZBWS4VS5GMXRDTN4D6CP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LWJ6A4ZBWS4VS5GMXRDTN4D6CP/action/storage_attestation","attest_author":"https://pith.science/pith/LWJ6A4ZBWS4VS5GMXRDTN4D6CP/action/author_attestation","sign_citation":"https://pith.science/pith/LWJ6A4ZBWS4VS5GMXRDTN4D6CP/action/citation_signature","submit_replication":"https://pith.science/pith/LWJ6A4ZBWS4VS5GMXRDTN4D6CP/action/replication_record"}},"created_at":"2026-07-05T07:17:12.760852+00:00","updated_at":"2026-07-05T07:17:12.760852+00:00"}